Towards Learning Efficient Maneuver Sets for Kinodynamic Motion Planning

TitleTowards Learning Efficient Maneuver Sets for Kinodynamic Motion Planning
Publication TypeReport
Year of Publication2019
AuthorsSivaramakrishnan, A, Littlefield, Z, Bekris, KE
Series Title7th ICAPS Workshop on Planning and Robotics (PlanRob)
Date Published07/2019

Planning for systems with dynamics is challenging as often there is no local planner available and the only primitive to explore the state space is forward propagation of controls. In this context, tree sampling-based planners have been developed, some of which achieve asymptotic optimality by propagating random controls during each iteration. While desirable for the analysis, random controls result in slow convergence to high quality trajectories in practice. This short position statement first argues that if a kinodynamic planner has access to local maneuvers that appropriately balance an exploitation-exploration trade-off, the planner’s per iteration performance is significantly improved. Furthermore, this work argues for the integration of modern machine learning frameworks with state-of-the-art, informed and asymptotically optimal kinodynamic planners. The proposed approach involves using using neural networks to infer local maneuvers for a robotic system with dynamics, which properly balance the above exploitation-exploration trade-off. Preliminary indications in simulated environments and systems are promising but also point to certain challenges that motivate further research in this direction.